lets_plot.geom_tile¶
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lets_plot.geom_tile(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, **other_args)¶ Display rectangles with x, y values mapped to the center of the tile.
- Parameters
mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.
data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.
stat (str, default=’identity’) – The statistical transformation to use on the data for this layer, as a string.
position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.
show_legend (bool, default=True) – False - do not show legend for this layer.
sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.
tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.
other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.
- Returns
Geom object specification.
- Return type
LayerSpec
Note
Understands the following aesthetics mappings:
x : x-axis coordinates of the center of rectangles.
y : y-axis coordinates of the center of rectangles.
alpha : transparency level of a layer. Understands numbers between 0 and 1.
color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.
fill : color of geometry filling.
size : lines width.
width : width of a tile.
height : height of a tile.
linetype : type of the line of tile’s border. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.
Examples
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import numpy as np from scipy.stats import multivariate_normal from lets_plot import * LetsPlot.setup_html() n = 100 a, b = -1, 0 x = np.linspace(-3, 3, n) y = np.linspace(-3, 3, n) X, Y = np.meshgrid(x, y) Z = np.exp(-5 * np.abs(Y ** 2 - X ** 3 - a * X - b)) data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()} ggplot(data, aes(x='x', y='y', color='z', fill='z')) + geom_tile()
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import numpy as np from scipy.stats import multivariate_normal from lets_plot import * LetsPlot.setup_html() np.random.seed(42) n = 25 x = np.linspace(-1, 1, n) y = np.linspace(-1, 1, n) X, Y = np.meshgrid(x, y) mean = np.zeros(2) cov = [[1, -.5], [-.5, 1]] rv = multivariate_normal(mean, cov) Z = rv.pdf(np.dstack((X, Y))) data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()} ggplot(data, aes(x='x', y='y')) + \ geom_tile(aes(fill='z'), width=.8, height=.8, color='black') + \ scale_fill_gradient(low='yellow', high='darkgreen')
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import numpy as np from lets_plot import * LetsPlot.setup_html() np.random.seed(42) data = {var: np.random.uniform(size=10) for var in 'abcd'} ggplot(data) + \ geom_tile(aes(fill='..corr..'), stat='corr', tooltips='none', color='white') + \ geom_text(aes(label='..corr..'), stat='corr', color='white') + \ scale_fill_brewer(type='div', palette='RdBu', breaks=[-1, -.5, 0, .5, 1])